Bioinformatics, 2010-06, Vol.26 (12), p.1572-1573
2010
Details
- Autor(en) / Beteiligte
- Titel
- ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking
- Ist Teil von
- Bioinformatics, 2010-06, Vol.26 (12), p.1572-1573
- Ort / Verlag
- Oxford: Oxford University Press
- Erscheinungsjahr
- 2010
- Link zum Volltext
- Quelle
- Free E-Journal (出版社公開部分のみ）
- Beschreibungen/Notizen
- Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1367-4803eISSN: 1460-2059, 1367-4811DOI: 10.1093/bioinformatics/btq170Titel-ID: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_2881355
- Format
- –
- Schlagworte
- Algorithms, Applications Note, Biological and medical sciences, Cluster Analysis, Data Mining - methods, Fundamental and applied biological sciences. Psychology, Gene Expression Profiling - methods, General aspects, Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects), Principal Component Analysis, Software